Large-scale tomb murals are divided into several blocks by narrow passages. Hence, during high-definition collection, certain information around these blocks may be missing. To address this, a digital generation technology for tomb murals based on a multiscale cascade network is proposed, for reconstructing these lost data between mural blocks. In this approach, tomb murals were first generated on a large scale, and the reconstruction results were subsequently input into a deep semantic small-scale generation network to generate fine digital information. A self-attention mechanism was introduced into the small-scale generation network to enhance the correlation between the generation region and the global information and solve the artifact problem of the generation region boundaries. In terms of the feedback loss, the texture loss and the texture fineness of the reconstructed information are improved, and the mural generation effect is also improved. Jump connections were added to the generation network to accelerate the training process and enhance the efficiency of gradient backpropagation. Based on ablation and comparative group experiments, the proposed digital generation technology can improve the texture matching rate of mural block epitaxial information and reduce the influence of artifacts. This proposed method achieves good objective indexes for the peak signal-to-noise ratio and structural similarity.